Files
Ares 4a5f1234bb fix: harden registry tooling, make tests hermetic, and restore metadata consistency (#168)
* chore: upgrade maintenance scripts to robust PyYAML parsing

- Replaces fragile regex frontmatter parsing with PyYAML/yaml library
- Ensures multi-line descriptions and complex characters are handled safely
- Normalizes quoting and field ordering across all maintenance scripts
- Updates validator to strictly enforce description quality

* fix: restore and refine truncated skill descriptions

- Recovered 223+ truncated descriptions from git history (6.5.0 regression)
- Refined long descriptions into concise, complete sentences (<200 chars)
- Added missing descriptions for brainstorming and orchestration skills
- Manually fixed imagen skill description
- Resolved dangling links in competitor-alternatives skill

* chore: sync generated registry files and document fixes

- Regenerated skills index with normalized forward-slash paths
- Updated README and CATALOG to reflect restored descriptions
- Documented restoration and script improvements in CHANGELOG.md

* fix: restore missing skill and align metadata for full 955 count

- Renamed SKILL.MD to SKILL.md in andruia-skill-smith to ensure indexing
- Fixed risk level and missing section in andruia-skill-smith
- Synchronized all registry files for final 955 skill count

* chore(scripts): add cross-platform runners and hermetic test orchestration

* fix(scripts): harden utf-8 output and clone target writeability

* fix(skills): add missing date metadata for strict validation

* chore(index): sync generated metadata dates

* fix(catalog): normalize skill paths to prevent CI drift

* chore: sync generated registry files

* fix: enforce LF line endings for generated registry files
2026-03-01 09:38:25 +01:00

5.4 KiB

name, description, risk, source, date_added
name description risk source date_added
azure-storage-file-datalake-py Azure Data Lake Storage Gen2 SDK for Python. Use for hierarchical file systems, big data analytics, and file/directory operations. unknown community 2026-02-27

Azure Data Lake Storage Gen2 SDK for Python

Hierarchical file system for big data analytics workloads.

Installation

pip install azure-storage-file-datalake azure-identity

Environment Variables

AZURE_STORAGE_ACCOUNT_URL=https://<account>.dfs.core.windows.net

Authentication

from azure.identity import DefaultAzureCredential
from azure.storage.filedatalake import DataLakeServiceClient

credential = DefaultAzureCredential()
account_url = "https://<account>.dfs.core.windows.net"

service_client = DataLakeServiceClient(account_url=account_url, credential=credential)

Client Hierarchy

Client Purpose
DataLakeServiceClient Account-level operations
FileSystemClient Container (file system) operations
DataLakeDirectoryClient Directory operations
DataLakeFileClient File operations

File System Operations

# Create file system (container)
file_system_client = service_client.create_file_system("myfilesystem")

# Get existing
file_system_client = service_client.get_file_system_client("myfilesystem")

# Delete
service_client.delete_file_system("myfilesystem")

# List file systems
for fs in service_client.list_file_systems():
    print(fs.name)

Directory Operations

file_system_client = service_client.get_file_system_client("myfilesystem")

# Create directory
directory_client = file_system_client.create_directory("mydir")

# Create nested directories
directory_client = file_system_client.create_directory("path/to/nested/dir")

# Get directory client
directory_client = file_system_client.get_directory_client("mydir")

# Delete directory
directory_client.delete_directory()

# Rename/move directory
directory_client.rename_directory(new_name="myfilesystem/newname")

File Operations

Upload File

# Get file client
file_client = file_system_client.get_file_client("path/to/file.txt")

# Upload from local file
with open("local-file.txt", "rb") as data:
    file_client.upload_data(data, overwrite=True)

# Upload bytes
file_client.upload_data(b"Hello, Data Lake!", overwrite=True)

# Append data (for large files)
file_client.append_data(data=b"chunk1", offset=0, length=6)
file_client.append_data(data=b"chunk2", offset=6, length=6)
file_client.flush_data(12)  # Commit the data

Download File

file_client = file_system_client.get_file_client("path/to/file.txt")

# Download all content
download = file_client.download_file()
content = download.readall()

# Download to file
with open("downloaded.txt", "wb") as f:
    download = file_client.download_file()
    download.readinto(f)

# Download range
download = file_client.download_file(offset=0, length=100)

Delete File

file_client.delete_file()

List Contents

# List paths (files and directories)
for path in file_system_client.get_paths():
    print(f"{'DIR' if path.is_directory else 'FILE'}: {path.name}")

# List paths in directory
for path in file_system_client.get_paths(path="mydir"):
    print(path.name)

# Recursive listing
for path in file_system_client.get_paths(path="mydir", recursive=True):
    print(path.name)

File/Directory Properties

# Get properties
properties = file_client.get_file_properties()
print(f"Size: {properties.size}")
print(f"Last modified: {properties.last_modified}")

# Set metadata
file_client.set_metadata(metadata={"processed": "true"})

Access Control (ACL)

# Get ACL
acl = directory_client.get_access_control()
print(f"Owner: {acl['owner']}")
print(f"Permissions: {acl['permissions']}")

# Set ACL
directory_client.set_access_control(
    owner="user-id",
    permissions="rwxr-x---"
)

# Update ACL entries
from azure.storage.filedatalake import AccessControlChangeResult
directory_client.update_access_control_recursive(
    acl="user:user-id:rwx"
)

Async Client

from azure.storage.filedatalake.aio import DataLakeServiceClient
from azure.identity.aio import DefaultAzureCredential

async def datalake_operations():
    credential = DefaultAzureCredential()
    
    async with DataLakeServiceClient(
        account_url="https://<account>.dfs.core.windows.net",
        credential=credential
    ) as service_client:
        file_system_client = service_client.get_file_system_client("myfilesystem")
        file_client = file_system_client.get_file_client("test.txt")
        
        await file_client.upload_data(b"async content", overwrite=True)
        
        download = await file_client.download_file()
        content = await download.readall()

import asyncio
asyncio.run(datalake_operations())

Best Practices

  1. Use hierarchical namespace for file system semantics
  2. Use append_data + flush_data for large file uploads
  3. Set ACLs at directory level and inherit to children
  4. Use async client for high-throughput scenarios
  5. Use get_paths with recursive=True for full directory listing
  6. Set metadata for custom file attributes
  7. Consider Blob API for simple object storage use cases

When to Use

This skill is applicable to execute the workflow or actions described in the overview.